Model learning. We use an ensemble of \(N\)  functions to approximate the model of the environment \(\lbrace f_{\rho _1}, ..., f_{\rho _N}\rbrace \) . Each of these functions is a probabilistic deep neural network whose purpose is to approximate the system dynamics, namely the next state of the agent given the current state and action. Probabilistic model ensembles have been studied as a way of realizing Bayesian neural networks, for capturing the epistemic uncertainty when learning complex dynamics, in order to mitigate overfitting when using a single model [1]}. Specifically, an ensemble of models is randomly initialized within an observable space; hence, each model learns a different mapping of the dynamics. For generating predictions from the ensembles, we sample transitions from the elite networks, i.e. those with the lowest L2-loss in a validation set, during the model rollouts by sampling uniformly a model for every simulation.
